3-body XZ in p-space (not working)

calculations/XZ/3body_p_space.jl

@@ -0,0 +1,45 @@
+include("../../p_space.jl")
+include("../../EC.jl")
+
+using Arpack
+
+# target = 4.0766890719636875 - 0.012758927741074495im
+
+Λ = 0
+m = 1.0
+V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)
+
+vertices = [0, 2 - 0.2im, 3, 4] # TODO: real contour instead of Berggren basis
+subdivisions = [15, 10, 10]
+jmax = 4
+
+E_max = 40
+μω_global = 0.5
+
+@time "H0" H0, _ = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, true, true)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+@time "Vp" Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)
+
+training_ref = 2 + 0.5im
+
+exact_E = [4.076642792419057-0.012998408352259658im,
+    3.6129849325287-0.007397677539402868im,
+    3.145212908643357-0.0038660337822150753im,	
+    2.6729225739451596-0.0021090370393881063im,	
+    2.196385760253282-0.0010430088245526555im,	
+    1.7162659936896967-0.0004515351140200029,	
+    1.2329926791785895-0.00017698044022813525im]
+
+training_c = [-1.5 - 0.5im] .+ (randn(8) .+ 0.05im * randn(8))
+extrapolating_c = 0.0 : 0.2 : 1.2
+
+EC = affine_EC(H0, Vp)
+train!(EC, training_c; ref_eval=training_ref, CAEC=false)
+extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
+
+exportCSV(EC, "temp/3b_p_space_XZ.csv")
+plot(EC, "temp/3b_p_space_XZ.pdf")
+
+# Results: training points are all over the place, and extrapolated values are garbage.